基于IPSO-DE-MH算法的突发水污染事件预测模型参数识别  

PARAMETER IDENTIFICATION OF SUDDEN WATER POLLUTION ACCIDENTS BASED ON IPSO-DE-MH METHOD

作　　者：李锦锦 杨海东 LI Jinjin;YANG Haidong(School of Economics and Management,Fuzhou University,Fuzhou 350116,China)

机构地区：[1]福州大学经济与管理学院,福州350116

出　　处：《环境工程》2022年第6期70-76,115,共8页Environmental Engineering

基　　金：福建省自然科学基金面上项目(2020J01460);中国博士后特别资助项目(2018T110640,2016M602053)。

摘　　要：预测模型是有效应对突发水污染事件的前提与基础。为了提高预测模型的准确性,提出了一种新的参数识别方法。首先从反问题与贝叶斯估计的视角构建突发水污染事件预测模型;然后在Metropolis-Hastings抽样方法的基础上,引入混沌理论、粒子群算法、微分进化算法等的思想,设计了一种新的参数识别方法,即IPSO-DE-MH算法;最后通过数值分析验证所设计方法的有效性和准确性。结果表明:新方法能较好地识别模型参数,为突发事件应急预测模型的快速构建提供了新思路。The prediction model is the premise and foundation of effectively dealing with sudden water pollution accidents. To improve the accuracy of the prediction model, a new parameters identification method was proposed in this paper. This paper first built a prediction model from the perspective of the inverse problem and Bayesian, and then designed a new identification method based on the chaos theory, particle swarm optimization, differential evolution and Metropolis-Hastings sampling method, i.e. IPSO-DE-MH. Finally, the effectiveness and accuracy of the designed method were verified by numerical analysis. The results showed that the new method could better identify the model parameters, and provide a new idea for the construction of an emergency prediction model.

关 键 词：参数识别 粒子群算法 微分进化算法 Metropolis-Hastings抽样方法 混沌理论 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] X52[自动化与计算机技术—控制科学与工程] X507[环境科学与工程—环境工程]